Pericarp thickness of sorghum whole grain is accurately predicted by NIRS and can affect the prediction of other grain quality parameters. (May 2016)

• Record Type: Journal Article
• Authors: Guindo, Diarah; Davrieux, Fabrice; Teme, Niaba; Vaksmann, Michel; Doumbia, Mohamed; Fliedel, Geneviève; Bastianelli, Denis; Verdeil, Jean-Luc; Mestres, Christian; Kouressy, Mamoutou; Courtois, Brigitte; Rami, Jean-Francois
• Is Part Of: Journal of cereal science. Volume 69(2016)
• Page Start: 218
• Page End: 227
• Publication Date: 2016-05
• Subjects: Sorghum grain -- Pericarp -- NIRS; Grain -- Periodicals; Cereal products -- Periodicals; Céréales -- Périodiques; Produits céréaliers -- Périodiques; Cereal products; Grain; Periodicals; 664.705
• DOI: 10.1016/j.jcs.2016.03.008
• Languages: English
• ISSNs: 0733-5210

Abstract

Abstract: The thickness of grain pericarp, the outer layer of the kernel, is an important breeding criterion for sorghum. This cereal is mainly used through traditional processing in family-based food systems in many regions of the world. We investigated in this study how pericarp thickness could be predicted by Near Infrared Reflectance Spectroscopy (NIRS), a fast and non-destructive measurement method that is commonly used to measure physico-chemical parameters of sorghum grains, and how this trait also influences the prediction of those parameters. We showed that, using a classification approach, it was possible to discriminate thick from thin pericarp whole grain samples with a good accuracy and that the proportion of thin and thick grains in mixed samples could also be predicted. In addition, pericarp thickness had a significant effect on the calibration performance for other grain parameters indicating that the pericarp can distort spectral information of whole grain samples. As a practical consequence, we suggest to develop separate whole grain calibration models for thin and thick pericarp samples, combined with a two-steps prediction approach to improve the accuracy of whole grain NIRS calibrations for grain quality parameters in sorghum. Highlights: High throughput discrimination between thick and thin pericarp sorghum grains by NIRS. The proportion of thick/thin pericarp grains can be predicted by NIRS quantification model. Thick pericarp disturbs the prediction of physico-chemical traits by NIRS of whole grain. A two-steps prediction approach can improve the accuracy of whole grain NIRS calibrations.